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논문 기본 정보

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학술저널
저자정보
조남준 (순천향대학교) Jeong Inyong (Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea) Kim Yeongmin (Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea) Kim Dong Ok (Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea) Ahn Se-Jin (Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea) Kang Sang-Hee (Department of Surgery, Korea University Guro Hospital, Seoul, Republic of Korea) 길효욱 (순천향대학교) Lee Hwamin (Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea)
저널정보
대한신장학회 Kidney Research and Clinical Practice Kidney Research and Clinical Practice Vol.43 No.4
발행연도
2024.7
수록면
538 - 547 (10page)
DOI
10.23876/j.krcp.23.330

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Background: Acute kidney injury (AKI) is a significant challenge in healthcare. While there are considerable researches dedicated to AKI patients, a crucial factor in their renal function recovery, is often overlooked. Thus, our study aims to address this issue through the development of a machine learning model to predict restoration of kidney function in patients with AKI. Methods: Our study encompassed data from 350,345 cases, derived from three hospitals. AKI was classified in accordance with the Kidney Disease: Improving Global Outcomes. Criteria for recovery were established as either a 33% decrease in serum creatinine levels at AKI onset, which was initially employed for the diagnosis of AKI. We employed various machine learning models, selecting 43 pertinent features for analysis. Results: Our analysis contained 7,041 and 2,929 patients’ data from internal cohort and external cohort respectively. The Categorical Boosting Model demonstrated significant predictive accuracy, as evidenced by an internal area under the receiver operating characteristic (AUROC) of 0.7860, and an external AUROC score of 0.7316, thereby confirming its robustness in predictive performance. SHapley Additive exPlanations (SHAP) values were employed to explain key factors impacting recovery of renal function in AKI patients. Conclusion: This study presented a machine learning approach for predicting renal function recovery in patients with AKI. The model performance was assessed across distinct hospital settings, which revealed its efficacy. Although the model exhibited favorable outcomes, the necessity for further enhancements and the incorporation of more diverse datasets is imperative for its application in real- world.

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